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@InProceedings{GEB2016,
  author        = {Gatys, Leon A. and Ecker, Alexander. S. and Bethge, Matthias},
  booktitle     = {{IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
  title         = {Image Style Transfer Using Convolutional Neural Networks},
  year          = {2016},
  abstract      = {Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.},
  archiveprefix = {arXiv},
  doi           = {10.1109/CVPR.2016.265},
  eprint        = {1508.06576},
}

@Article{SZ2014,
  author        = {Simonyan, Karen and Zisserman, Andrew},
  journal       = {Computing Research Repository ({CoRR})},
  title         = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
  year          = {2014},
  volume        = {abs/1409},
  abstract      = {In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.},
  archiveprefix = {arXiv},
  eprint        = {1409.1556},
}

@InProceedings{MV2015,
  author        = {Mahendran, Aravindh and Vedaldi, Andrea},
  booktitle     = {{IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
  title         = {Understanding Deep Image Representations by Inverting Them},
  year          = {2015},
  abstract      = {Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited. In this paper we conduct a direct analysis of the visual information contained in representations by asking the following question: given an encoding of an image, to which extent is it possible to reconstruct the image itself? To answer this question we contribute a general framework to invert representations. We show that this method can invert representations such as HOG and SIFT more accurately than recent alternatives while being applicable to CNNs too. We then use this technique to study the inverse of recent state-of-the-art CNN image representations for the first time. Among our findings, we show that several layers in CNNs retain photographically accurate information about the image, with different degrees of geometric and photometric invariance.},
  doi           = {10.1109/CVPR.2015.7299155},
  archiveprefix = {arXiv},
  eprint        = {1412.0035},
}

@InProceedings{LW2016,
  author        = {Li, Chuan and Wand, Michael},
  booktitle     = {{IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
  title         = {Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis},
  year          = {2016},
  abstract      = {This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN feature pyramid, controlling the image layout at an abstract level. We apply the method to both photographic and non-photo-realistic (artwork) synthesis tasks. The MRF regularizer prevents over-excitation artifacts and reduces implausible feature mixtures common to previous dCNN inversion approaches, permitting synthesizing photographic content with increased visual plausibility. Unlike standard MRF-based texture synthesis, the combined system can both match and adapt local features with considerable variability, yielding results far out of reach of classic generative MRF methods.},
  doi           = {10.1109/CVPR.2016.272},
  archiveprefix = {arXiv},
  eprint        = {1601.04589},
}

@InProceedings{GEB+2017,
  author        = {Gatys, Leon A. and Ecker, Alexander S. and Bethge, Matthias and Hertzmann, Aaron and Shechtman, Eli},
  booktitle     = {{IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})},
  title         = {Controlling Perceptual Factors in Neural Style Transfer},
  year          = {2017},
  abstract      = {Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer.},
  archiveprefix = {arXiv},
  doi           = {10.1109/CVPR.2017.397},
  eprint        = {1611.07865},
}

@InProceedings{SID2017,
  author    = {Semmo, Amir and Isenberg, Tobias and Döllner, Jürgen},
  booktitle = {Proceedings of the Symposium on Non-Photorealistic Animation and Rendering ({NPAR})},
  title     = {Neural Style Transfer: A Paradigm Shift for Image-Based Artistic Rendering?},
  year      = {2017},
  abstract  = {In this meta paper we discuss image-based artistic rendering (IB-AR) based on neural style transfer (NST) and argue, while NST may represent a paradigm shift for IB-AR, that it also has to evolve as an interactive tool that considers the design aspects and mechanisms of artwork production. IB-AR received significant attention in the past decades for visual communication, covering a plethora of techniques to mimic the appeal of artistic media. Example-based rendering represents one the most promising paradigms in IB-AR to (semi-)automatically simulate artistic media with high fidelity, but so far has been limited because it relies on pre-defined image pairs for training or informs only low-level image features for texture transfers. Advancements in deep learning showed to alleviate these limitations by matching content and style statistics via activations of neural network layers, thus making a generalized style transfer practicable. We categorize style transfers within the taxonomy of IB-AR, then propose a semiotic structure to derive a technical research agenda for NSTs with respect to the grand challenges of NPAR. We finally discuss the potentials of NSTs, thereby identifying applications such as casual creativity and art production.},
  doi       = {10.1145/3092919.3092920},
}


@InProceedings{JAL2016,
  author        = {Johnson, Justin and Alahi, Alexandre and Li, Fei-Fei},
  booktitle     = {European Conference on Computer Vision ({ECCV})},
  title         = {Perceptual Losses for Real-Time Style Transfer and Super-Resolution},
  year          = {2016},
  archiveprefix = {arXiv},
  doi           = {10.1007/978-3-319-46475-6_43},
  eprint        = {1603.08155},
}

@InProceedings{ULVL2016,
  author        = {Ulyanov, Dmitry and Lebedev, Vadim and Vedaldi, Andrea and Lempitsky, Viktor S.},
  booktitle     = {International Conference on Machine Learning ({ICML})},
  title         = {Texture Networks: Feed-forward Synthesis of Textures and Stylized Images},
  year          = {2016},
  archiveprefix = {arXiv},
  eprint        = {1603.03417},
  url           = {http://proceedings.mlr.press/v48/ulyanov16.html},
}

@Article{UVL2016,
  author        = {Ulyanov, Dmitry and Vedaldi, Andrea and Lempitsky, Viktor S.},
  journal       = {Computing Research Repository ({CoRR})},
  title         = {Instance Normalization: The Missing Ingredient for Fast Stylization},
  year          = {2016},
  archiveprefix = {arXiv},
  eprint        = {1607.08022},
}

@InProceedings{KSH2012,
  author    = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
  booktitle = {Advances in Neural Information Processing Systems 25 ({NIPS})},
  title     = {Imagenet Classification with Deep Convolutional Neural Networks},
  year      = {2012},
  abstract  = {We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet ILSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers,and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called “dropout” that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,compared to 26.2% achieved by the second-best entry.},
  url       = {https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf},
}

@InProceedings{CZP+2018,
  author    = {Chen, Liang-Chieh and Zhu, Yukun and Papandreou, George and Schroff, Florian and Adam, Hartwig},
  booktitle = {The European Conference on Computer Vision ({ECCV})},
  title     = {Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
  year      = {2018},
  doi       = {10.1007/978-3-030-01234-2_49},
  url       = {http://openaccess.thecvf.com/content_ECCV_2018/papers/Liang-Chieh_Chen_Encoder-Decoder_with_Atrous_ECCV_2018_paper.pdf},
}

@InProceedings{PGM+2019,
  author    = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  booktitle = {Advances in Neural Information Processing Systems 32 ({NIPS})},
  title     = {{PyTorch}: An Imperative Style, High-Performance Deep Learning Library},
  year      = {2019},
  url       = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf},
}

@Book{GG2001,
  author    = {Gooch, Bruce and Gooch, Amy},
  publisher = {A. K. Peters, Ltd.},
  title     = {Non-Photorealistic Rendering},
  year      = {2001},
  isbn      = {1568811330},
}

@Article{JYF+2019,
  author   = {Jing, Yongcheng and Yang, Yezhou and Feng, Zunlei and Ye, Jingwen and Yu, Yizhou and Song, Mingli},
  journal  = {{IEEE} Transactions on Visualization and Computer Graphics},
  title    = {Neural Style Transfer: A Review},
  year     = {2019},
  abstract = {The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://osf.io/f8tu4/.},
  doi      = {10.1109/TVCG.2019.2921336},
}

@Article{ML2020,
  author  = {Meier, Philip and Lohweg, Volker},
  journal = {Journal of Open Source Software {JOSS}},
  title   = {pystiche: A Framework for Neural Style Transfer},
  year    = {2020},
  doi     = {10.21105/joss.02761},
}

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